Yes, exactly, I would like to have numerical formula of line separating
different areas.
unless you use a linear kernel there is no simple analytical formula
and how to do that for linear kernel? In first analysis I think linear
will be enough for me. In attachment I send script and input data, it's
quite easy sample, because I have only two different groups, but for
first try it will be enough for me
Thanks,
GS
Alex
------------------------------------------------------------------------------
All the data continuously generated in your IT infrastructure contains a
definitive record of customers, application performance, security
threats, fraudulent activity and more. Splunk takes this data and makes
sense of it. Business sense. IT sense. Common sense.
http://p.sf.net/sfu/splunk-d2dcopy1
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import numpy as np
import pylab as pl
from scikits.learn import svm, datasets
# import some data to play with
#iris = datasets.load_iris()
#X = iris.data[:, :2] # we only take the first two features. We could
# avoid this ugly slicing by using a two-dim dataset
#Y = iris.target
#two sets:
#1: fore, AGB, possible AGB, multpile
#2: back, post-AGB, YSO
Y = np.loadtxt("/home/qrczak/software/scikits.learn-0.8.1/examples/svm/not_identified/N3-S7vsn3_y.txt").astype("int").T
X = np.loadtxt("/home/qrczak/software/scikits.learn-0.8.1/examples/svm/not_identified/N3-S7vsn3_x.txt", delimiter=",")
#Xniez = np.loadtxt("/home/qrczak/software/scikits.learn-0.8.1/examples/svm/not_identified/N3-S7vsn3_not_identified_x.txt", delimiter=",")
#Yniez = np.loadtxt("/home/qrczak/software/scikits.learn-0.8.1/examples/svm/not_identified/N3-S7vsn3_not_identified_y.txt").astype("int").T
def my_kernel(x, y):
"""
We create a custom kernel:
(2 0)
k(x, y) = x ( ) y.T
(0 1)
"""
M = np.array([[2, 0], [0, 1.0]])
return np.dot(np.dot(x, M), y.T)
h=.02 # step size in the mesh
# we create an instance of SVM and fit out data.
#clf = svm.SVC(kernel=my_kernel)
clf = svm.SVC(kernel='linear', C=1.0, probability=False, degree=3, coef0=0.0, tol=0.001,
cache_size=100.0, shrinking=True, gamma=0.25)
clf.fit(X, Y)
# Plot the decision boundary. For that, we will asign a color to each
# point in the mesh [x_min, m_max]x[y_min, y_max].
x_min, x_max = X[:,0].min()-1, X[:,0].max()+1
y_min, y_max = X[:,1].min()-1, X[:,1].max()+1.5
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
# Put the result into a color plot
Z = Z.reshape(xx.shape)
pl.set_cmap(pl.cm.Paired)
pl.pcolormesh(xx, yy, Z)
#cs = pl.contour(xx, yy, Z)
# Plot also the training points
pl.scatter(X[:,0], X[:,1], c=Y)
#pl.scatter(Xniez[:,0], Xniez[:,1], c=Yniez, color = '#eeefff')
#print Y
#pl.title('3-Class classification using Support Vector Machine with custom kernel')
ax = pl.gca()
ax.set_ylim(ax.get_ylim()[::-1])
pl.xlabel('N3-S7')
pl.ylabel('N3')
pl.axis('tight')
pl.show()
------------------------------------------------------------------------------
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